RAG / 01

RAG, LLM & Enterprise Knowledge

We connect documents, databases, ticket systems, and product knowledge through one retrieval layer. Retrieval supplies permission-filtered evidence; generation produces an answer and citations grounded in that evidence. Because citations alone do not guarantee correctness, we evaluate both layers separately.

Discuss your project

Runtime flowRAG / 01

01Sources
02Permission filter
03Retrieval
04Generation
05Cited answer

The problem

We define the problem before the solution.

Enterprise knowledge rarely lives in one place. Contracts sit on a file server, product knowledge in a wiki, customer history in a ticket system, and the rules in a PDF nobody has updated. When someone has to search all of them to reach a correct answer, the problem is not model intelligence; it is that the knowledge is not reachable.

This is where RAG belongs: the model retrieves relevant evidence before answering and grounds the answer in it. But a RAG system that survives production is more than documents in a vector database. Chunking strategy, metadata, access permissions, hybrid search, reranking, and citation quality are designed separately and measured separately. We build these layers as one retrieval architecture and verify its quality with an evaluation set built from real questions.

Use cases

Where this system usually shows up.

Enterprise knowledge assistant

An internal assistant that brings scattered documentation, wikis, and ticket history into one question-and-answer surface, showing which document each answer came from.

Support copilot

A layer that lets a support team find a verifiable answer across past resolutions, product docs, and policy text in seconds.

Contract and compliance search

Search that locates and quotes the relevant clause inside long legal or regulatory text, returning sources rather than interpretation.

Technical documentation assistant

A documentation layer that keeps version-specific technical knowledge current and distinguishes answers that belong to older releases.

What does the system deliver?

Answers that can be checked against cited sources

Role-aware knowledge access

Enterprise search grounded in current data

Delivery scope

We deliver an operable product, not just a model.

01
Source and data-readiness analysis
02
Chunking, embedding, and retrieval architecture
03
Evaluation dataset and quality dashboard
04
API, interface, and enterprise integrations

How we build it

We design technical components together with the operating workflow.

Connect sources

We bring documents, databases, wikis, and tickets into a secure ingestion layer.

Design retrieval

We design search, reranking, metadata, and permission filters around the use case.

Measure quality

We measure accuracy, citation fidelity, latency, and cost using real questions.

Productionize

We launch with monitoring, feedback loops, and version control.

Technical approach

The layers and tools we work with.

Retrieval: Hybrid search, reranking, metadata and permission filters
Embedding: BGE-M3 and multilingual embedding models
Serving: FastAPI, Docker, Qdrant, PostgreSQL
Evaluation: RAGAS, Langfuse, and project-specific evaluation sets

Frequently asked questions

What to know before making a decision.

01Should we use RAG or fine-tuning?

RAG is usually evaluated first for current, private enterprise knowledge. Fine-tuning becomes relevant when behavior, format, or task performance must change.

02Can the data remain inside our environment?

Yes. We can design the architecture for public cloud, private cloud, on-premise, or hybrid deployment.

03How do we notice when the system answers incorrectly?

A citation alone is not proof of correctness; an answer can cite the right source and still reason incorrectly. So we build an evaluation set from real questions, measure retrieval hit rate and answer-to-source faithfulness separately, and feed user feedback into the same measurement.

04How long until it is live?

Data readiness sets the timeline, not the model. Where sources are reachable, formats are consistent, and the permission model is clear, a measurable pilot can be stood up quickly. Scanned documents, conflicting versions, or unclear access rules move the schedule; discovery is where we establish which case you are in.

05When is RAG the wrong choice?

If the answer can be fetched from one structured database with a query, if the knowledge already lives in a single current source, or if the real need is to change the model's behavior and output format, RAG adds complexity without value. We say so during discovery and recommend the simpler path.